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Albert, A, Hallowell, M R and Kleiner, B M (2014) Enhancing Construction Hazard Recognition and Communication with Energy-Based Cognitive Mnemonics and Safety Meeting Maturity Model: Multiple Baseline Study. Journal of Construction Engineering and Management, 140(02).
Arashpour, M, Wakefield, R, Blismas, N and Lee, E W M (2014) Analysis of Disruptions Caused by Construction Field Rework on Productivity in Residential Projects. Journal of Construction Engineering and Management, 140(02).
Brockman, J L (2014) Interpersonal Conflict in Construction: Cost, Cause, and Consequence. Journal of Construction Engineering and Management, 140(02).
Chen, Z, Abdullah, A B, Anumba, C J and Li, H (2014) ANP Experiment for Demolition Plan Evaluation. Journal of Construction Engineering and Management, 140(02).
Chong, D, Wang, Y, Guo, H and Lu, Y (2014) Volatile Organic Compounds Generated in Asphalt Pavement Construction and Their Health Effects on Workers. Journal of Construction Engineering and Management, 140(02).
Dehghan, R and Ruwnapura, J Y (2014) Model of Trade-Off between Overlapping and Rework of Design Activities. Journal of Construction Engineering and Management, 140(02).
Deng, F and Smyth, H (2014) Nature of Firm Performance in Construction. Journal of Construction Engineering and Management, 140(02).
Jafarzadeh, R, Ingham, J M, Wilkinson, S, González, V and Aghakouchak, A A (2014) Application of Artificial Neural Network Methodology for Predicting Seismic Retrofit Construction Costs. Journal of Construction Engineering and Management, 140(02).
- Type: Journal Article
- Keywords: Construction costs; Seismic effects; Rehabilitation; Neural networks; Construction cost estimation; Seismic retrofit projects; Cost modeling; Artificial neural networks;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000725
- Abstract:
Following an extensive literature review, it was established that professional subjective judgment and regression analysis were the two main techniques utilized for predicting the seismic retrofit construction cost. The study presented in this paper aims at predicting this cost by employing a more advanced modeling technique known as the artificial neural network (ANN) methodology. Using this methodology, a series of nonparametric ANN models was developed based on significant predictors of the retrofit net construction cost (RNCC). Data on these predictors, together with the RNCC, were collected from 158 earthquake-prone public school buildings, each having a framed structure. A novel systematic framework was proposed with the aim to increase the generalization ability of ANN models. Using this framework, the values of critical components involved in the design of ANN models were defined. These components included the number of hidden layers and neurons, and learning parameters in terms of learning rate and momentum. The sensitivity of the developed ANN models to these components was examined, and it was found that the predictive performance of these models was more influenced by the number of hidden neurons than by the value of learning parameters. Also, the results of this examination revealed that the overlearning problem became more serious with an increase in the number of predictors. In addition to the framework proposed for the successful development of ANN models, the primary contribution of this study to the construction industry is the introduction of building total area as the key predictor of the RNCC. This predictor enables a reliable estimation of the RNCC to be made at the early development stage of a seismic retrofit project when little information is known about the project.
Kasapoğlu, E (2014) Leadership Styles in Architectural Design Offices in Turkey. Journal of Construction Engineering and Management, 140(02).
Khalili, A and Chua, D K (2014) Integrated Prefabrication Configuration and Component Grouping for Resource Optimization of Precast Production. Journal of Construction Engineering and Management, 140(02).
Lopez del Puerto, C, Clevenger, C M, Boremann, K and Gilkey, D P (2014) Exploratory Study to Identify Perceptions of Safety and Risk among Residential Latino Construction Workers as Distinct from Commercial and Heavy Civil Construction Workers. Journal of Construction Engineering and Management, 140(02).
Lu, W, Ye, K, Flanagan, R and Jewell, C (2014) Nexus between Contracting and Construction Professional Service Businesses: Empirical Evidence from International Market. Journal of Construction Engineering and Management, 140(02).
Martin, H and Lewis, T M (2014) Pinpointing Safety Leadership Factors for Safe Construction Sites in Trinidad and Tobago. Journal of Construction Engineering and Management, 140(02).
Rosenbaum, S, Toledo, M and González, V (2014) Improving Environmental and Production Performance in Construction Projects Using Value-Stream Mapping: Case Study. Journal of Construction Engineering and Management, 140(02).
Stamatiadis, N, Goodrum, P, Shocklee, E and Wang, C (2014) Quantitative Analysis of State Transportation Agency’s Experience with Constructability Reviews. Journal of Construction Engineering and Management, 140(02).
Zhao, T and Dungan, J M (2014) Improved Baseline Method to Calculate Lost Construction Productivity. Journal of Construction Engineering and Management, 140(02).